code: def forward(self, inputs: torch.Tensor) -> torch.Tensor:
      # shape notation
      # i_s: input_size
      # s: size
      # b_s: batch_size
  
      # inputs shape [b_s, i_s]
      hidden = self.blocks[0](inputs)  # [b_s, s]
      for n in range(1, self.num_total_blocks):
          hidden = (self.blocks[n](hidden) + hidden) * (
              0.5**0.5
          )  # [b_s, s]
      return hidden
  
imports: from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from ludwig.modules.normalization_modules import GhostBatchNormalization
  from ludwig.utils.entmax import Entmax15, EntmaxBisect, Sparsemax
  from ludwig.utils.torch_utils import LudwigModule
label_desc: Use image node_id 2 for calling the FeatureTransformer obj's underlying member method code with example usage: obj.forward(inputs) and returns: hidden